An Institutional Perspective on Genres: Generic Subtitles in German Literature from 1500-2020
Bibliographic record
Abstract
Using a custom-designed database of 388,000 first editions of German Literature this paper investigates the long-term development of genre-indicating subtitles over more than 500 years of literary history. This approach adds a social-institutional perspective to recent work in the field of genre theory, and is a first step towards combining historical testimony, i.e. historical actors’ classifications, and textual features in a single model. Starting from the fundamental question of how many books have generic subtitles, the paper analyses the use of the most common genre labels, the relation between generic subtitles and genre production, periods of the permanent presence of generic terms (institutional cycles) and periods of generic differentiation. It identifies recurrent patterns in the development of generic subtitles using K-Means-Clustering and Dynamic Time Warping (DTW) and sheds light on literature’s changing relation to history and truth, thereby underpinning recent theoretical work on the practices of poetic invention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".